An Evolutionary Approach to Drug-Design Using Quantam Binary Particle Swarm Optimization Algorithm

The present work provides a new approach to evolve ligand structures which represent possible drug to be docked to the active site of the target protein. The structure is represented as a tree where each non-empty node represents a functional group. It is assumed that the active site configuration of the target protein is known with position of the essential residues. In this paper the interaction energy of the ligands with the protein target is minimized. Moreover, the size of the tree is difficult to obtain and it will be different for different active sites. To overcome the difficulty, a variable tree size configuration is used for designing ligands. The optimization is done using a quantum discrete PSO. The result using fixed length and variable length configuration are compared.Comment: 4 pages, 6 figures (Published in IEEE SCEECS 2012). arXiv admin note: substantial text overlap with arXiv:1205.641

Shepherding Distributions for parallel Markov Chain Monte Carlo

One of the major concerns for Markov Chain Monte Carlo (MCMC) algorithms is that they can take a long time to converge to the desired stationary distribution. In practice, MCMC algorithms may take to millions of iterations to converge to the target distribution, requiring a wall clock time measured in months. This thesis presents a general algorithmic framework for running MCMC algorithms in a parallel/distributed environment, that can result in faster burn-in leading to convergence to the target distribution. Our framework, which we call the method of "shepherding distributions", relies on the introduction of an auxiliary distribution called a shepherding distribution (SD) that uses several MCMC chains running in parallel. These chains collectively explore the space of samples, communicating via the shepherding distribution, to reach high likelihood regions faster. We consider various scenarios where shepherding distributions can be used, including the case where several machines or CPU cores work on the same data in parallel (the so-called transition parallel application of the framework) and the case where a large data set itself can be partitioned across several machines or CPU cores and various chains work on subsets of the data (the so-called data parallel application of the framework). This latter application is particularly useful in solving "big data" Machine Learning problems. Experiments under both scenarios illustrate the effectiveness of our shepherding approach to MCMC parallelization

A hybrid discrete differential evolution algorithm for economic lot scheduling problem with time variant lot sizing

This article presents an efficient Hybrid Discrete Differential Evolution (HDDE) model to solve the Economic Lot Scheduling Problem (ELSP) using a time variant lot sizing approach. This proposed method introduces a novel Greedy Reordering Local Search (GRLS) operator as well as a novel Discrete DE scheme for solving the problem. The economic lot-scheduling problem (ELSP) is an important production scheduling problem that has been intensively studied. In this problem, several products compete for the use of a single machine, which is very similar to the real-life industrial scenario, in particular in the field of remanufacturing. The experimental results indicate that the proposed algorithm outperforms several previously used heuristic algorithms under the time-varying lot sizing approach